Got First 500 Users for your AI product as an Indie-（To be continued)
Source: Dev.to
My Indie Journey So Far
Today I checked our user data:
SELECT COUNT(*) FROM users;It returned a single number: 541.
You might not think that’s much, but for my first indie project it’s a number I’m proud of. Since launching on October 10, 2024, this number has been growing steadily and organically—we haven’t done much active promotion.
What You’ll Find in This Post
- Ideation – How we turned a friend’s casual request into an AI‑powered stock‑analysis product.
- 0 → 10 users – Validating ideas with minimal cost, even without putting the product online.
- 10 → 100 users – Riding market momentum and media buzz for organic growth.
- 100 → 500 users – How good user experience drives user growth.
- Monetization – Key points and how to get started.
(It’s a bit long, so I may split it into two posts.)
The Spark
Last July an open‑source project called Trading Agents suddenly blew up on GitHub. Its social‑media performance was equally dramatic—typical of that style. While I’m not a fan of hype‑driven marketing, I have to admit it makes technical concepts accessible to the masses.
A friend of mine, a fund manager, asked if I could get this open‑source project running so he could use it for stock analysis.
“Just fork it, deploy it, run it—should be easy, right?”
Yeah, I thought so too at first.
The Hard Part: Data Acquisition
I quickly discovered the hardest part was data acquisition—you need real‑time stock quotes, company financials, news data, and more. After a couple of days of figuring it out, I finally generated the reports.
My friend then asked me every few days to run analysis on different stocks. That’s when I realized there was a real gap:
The technology to analyze stocks with AI exists, but regular investors can’t access it.
If I could package this capability into a product where people just open a webpage and use it, there should be demand.
Lessons Learned So Far
- Technology only creates commercial value when it’s properly packaged into good products.
- The things tech nerds often disdain are exactly where the commercial value lies.
I didn’t realize this early on—I’m still in the process of this mental shift.
Our Team
- Two‑person indie dev team
- Partner 1: technical architecture & back‑end development
- Partner 2: product design & front‑end development
- No strict role divisions (finding a co‑founder is a topic for another time).
Early Experiments
We started with the simplest version:
- Run some popular stocks daily.
- Post the plain‑text reports to stock forums to gauge reactions.
We posted for over ten days straight—no traction.
The Turning Point
We changed the format: convert numbers into visual dashboards.
That single change made a huge difference:
- People started paying attention.
- Discussions picked up.
- Users asked, “Bro, where did you get this chart?”
From a user perspective, the visual format created a completely different reaction, even though it was trivially easy for us developers to implement.
Formal Development (v0.1)
Core function:
- Enter a stock ticker.
- Wait a few minutes.
- Read the AI‑generated report.
No complex parameter settings, fancy UI, or user system.
Timeline
- Estimated with AI coding tools: 1 week to launch.
- Actual time: 1 month.
Why the delay?
Even though the feature set was simple, we spent considerable effort on:
| Task | Description | Time Allocation |
|---|---|---|
| Stable data acquisition | Switched from real‑time API calls (which hurt performance) to stored data; used web‑scraping or purchased feeds. | 60 % |
| Prompt optimization | Fixed issues in the original open‑source prompts and performed targeted improvements. | 20 % |
| Landing‑page polish | First impression matters—3 seconds to get a click. | 30 % |
Launch & Early Growth
- October 9, 2024: Product launched online.
- Announced on social media: “Built something, give it a try.”
First week: 10+ registrations (mostly friends or friends‑of‑friends).
- Average session length: 10 minutes per person.
- Average stocks queried: 7 (one page per stock).
Second week: A hot event on social media—Alpha‑Arena gave several LLMs $10 000 to trade Bitcoin and showed live results.
The experiment made “AI investing” blow up. Many people started wondering: Can AI really help with investment decisions?
Our Response
We rode the wave and published a deep‑dive article titled:
“When AI Can Trade Crypto, How Should We Use AI for Stocks?”
- Spent two full days writing it.
- Back‑tested AlphaWiseWin’s recommendations on a few stocks (AAPL, CEG, KO).
- Using ~10 days of data, our simulated returns still beat the market.
The article attracted attention. By early November, we crossed 100 registered users. The two spikes in our growth chart correspond to our posts about the Alpha‑Arena event.
Current Status (as of today)
- Registered users: 541
- Growth: Steady and organic, driven mainly by content and visual presentation.
What’s Next?
- Continue polishing the UI/UX.
- Expand data sources and improve prompt engineering.
- Explore monetization strategies (see the “Monetization” section above).
Stay tuned for the next post where I’ll dive deeper into scaling from 100 → 500 users and the monetization roadmap.
Progress Update
User Base: 0 users (initial stage)
Feedback Channels: Email (both good and bad)
Good Feedback
- Reports are genuinely useful and provide comprehensive angles.
- More reliable than randomly reading news.
- One user realized how many risk factors their stock had after seeing our analysis.
Bad Feedback
- Speed: Reports take several minutes to generate.
- Clarity: AI analysis can be too academic and hard to understand.
- Depth: Users want more data (financial metrics, valuation comparisons, etc.).
Immediate Action: Speed Improvement
We tackled the “too slow” issue first, reducing average generation time from ≈10 minutes to ≈4 minutes.
Methods used
- Workflow optimization – switched serial nodes to parallel processing.
- LLM API tuning – tested different response‑speed configurations.
- Caching – leveraged cached results where possible.
These changes weren’t hard; they just required repeated refinement.
Note: Up to this point we didn’t even have a user system, let alone a commercial charging model. That was our next consideration.
Looking Ahead: Version 1.0
Entering November, we identified further opportunities in visualization. We decided the upcoming release would be the real Version 1.0, guided by a single core principle:
“Let users understand a stock at a glance.”
Why the previous approach created friction
-
Many products give users tons of options, forcing them to ask questions such as:
- Include technical analysis?
- Deep‑dive into financials?
- Focus on short‑term or long‑term?
-
This flexibility actually increases friction because:
- Most users don’t know how to comprehensively analyze a stock.
- Multiple interactions are needed to build a complete understanding.
Our new approach
- One‑click generation of a comprehensive report.
Two keywords
- One click
- Comprehensive
Impact of the Change
- Immediate boost in user engagement.
- Returning‑user frequency increased.
- By mid‑December we reached a stable 20 % weekly retention rate.
Reflections & Next Steps
- We’ve moved past the MVP stage and are now considering monetization.
- Key learning: Making the technicals easy to use is more important than we thought.
Next post: I’ll share our thoughts on monetization—though we’re still on the path, there’s already valuable insight to discuss.